slug
Convert text to URL-friendly slug.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to slugify |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | ||
| original | Yes |
Convert text to URL-friendly slug.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to slugify |
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | ||
| original | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full burden for behavioral disclosure. It does not explain how it handles special characters, whitespace, case conversion, or Unicode, all typical concerns for slug generation. The minimal description lacks sufficient detail for safe autonomous use.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that conveys the core function without extraneous words. It is appropriately brief for a straightforward utility tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with one parameter and an output schema (not shown but indicated as present). The description is sufficient for a basic transformation, though additional context about edge cases (e.g., empty input) would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a single parameter 'text' having a clear description 'The text to slugify'. The description adds no extra meaning beyond what the schema already provides, meeting the baseline for high-coverage cases.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Convert text to URL-friendly slug' uses specific verb 'convert' and resource 'text', clearly stating the tool's function. It distinguishes from siblings like 'deslugify' (reverse operation) and 'is_valid_slug' (validation), making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'slugify' (a sibling with similar name). No conditional or exclusionary language is present, leaving the agent to infer context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many tools have overlapping purposes, such as multiple random generators (random_integer, random_number), duplicate hashing functions (hash_md5, md5_checksum), and near-identical tools (compare, compare_2, compare_decimals). The sheer number of tools and lack of clear boundaries make it difficult for an agent to differentiate.
Naming is highly inconsistent. There are duplicate tools with different names (camel_case vs to_camel_case, slug vs slugify), arbitrary suffixes like '_2', and mixing of patterns (e.g., generate_password vs password_entropy). No clear convention is followed.
With 572 tools, the server is massively overpopulated for any coherent purpose. It includes trivial endpoints (true_endpoint, null, hello_world) and numerous duplicates, far exceeding a well-scoped utility set.
While the server covers many domains (math, strings, dates, colors, etc.), the presence of duplicate and trivial tools indicates a lack of thoughtful curation. There are gaps in basic operations (e.g., no dedicated file or network tools), and many tools are redundant.